Topic
machine-learning
12 resources tagged “machine-learning”.
Artificial Intelligence: Foundations of Computational Agents
David L. Poole, Alan K. Mackworth
An undergraduate/graduate AI textbook organized around the design of intelligent agents: search, reasoning under uncertainty, planning, learning, and multiagent systems, presented within one coherent agent framework.
Artificial IntelligenceAdvancedCreative CommonsHTML
Computer Vision: Algorithms and Applications
Richard Szeliski
A comprehensive computer vision textbook covering image formation, feature detection, segmentation, structure from motion, stereo, 3D reconstruction, and recognition — with deep-learning approaches added in the 2nd edition.
Computer VisionAdvancedOfficial Free DistributionPDF
Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville
The widely cited reference on deep learning: the mathematical background, deep feedforward networks, regularization and optimization, convolutional and recurrent networks, and research topics including generative models.
Deep LearningAdvancedMITHTML
Foundations of Data Science
Avrim Blum, John Hopcroft, Ravindran Kannan
The mathematical and algorithmic underpinnings of data science: high-dimensional geometry, singular value decomposition, random graphs, clustering, and machine-learning theory, aimed at readers who want to understand why the methods work.
Machine LearningAdvancedOfficial Free DistributionPDF · 3.5 MBMachine Learning Crash Course
Google
Google's practical, example-driven introduction to machine learning: loss and gradient descent, classification, neural networks, embeddings, fairness, and production ML systems, with interactive exercises using TensorFlow.
Machine LearningBeginnerAPACHE 2.0HTML
An Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
A broad, applied introduction to statistical learning: linear and logistic regression, resampling, regularization, trees and ensembles, support vector machines, and unsupervised learning, with lab exercises in R and Python.
Machine LearningIntermediateOfficial Free DistributionPDF
Mathematics for Machine Learning
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
The mathematical foundations behind machine learning, taught for ML rather than in the abstract: linear algebra, analytic geometry, matrix decompositions, vector calculus, probability, and optimization, connected to real ML models.
Machine LearningAdvancedOfficial Free DistributionPDF · 17 MBNeural Networks and Deep Learning
Michael Nielsen
A hands-on introduction to neural networks, built around teaching a network to recognize handwritten digits. Derives backpropagation from first principles, then covers why deep networks are hard to train and the ideas that make deep learning work.
Deep LearningIntermediateCreative CommonsHTML
Natural Language Processing with Python
Steven Bird, Ewan Klein, Edward Loper
A practical introduction to natural language processing using Python and NLTK: text processing, tagging, classification, parsing grammars, and information extraction, with runnable examples throughout.
Natural Language ProcessingIntermediateCreative CommonsHTML
Python Data Science Handbook
Jake VanderPlas
A practical guide to the core Python data-science stack: IPython/Jupyter, NumPy, Pandas, Matplotlib, and Scikit-Learn, with real workflows for cleaning, transforming, visualizing, and modeling data.
Machine LearningIntermediateMITHTML
Speech and Language Processing
Daniel Jurafsky, James H. Martin
The standard graduate textbook on NLP and computational linguistics: language models, part-of-speech tagging, parsing, information extraction, semantics, and speech recognition. The freely available 3rd edition draft is continuously updated.
Natural Language ProcessingAdvancedOfficial Free DistributionPDF
Dive into Deep Learning
Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
An interactive deep learning textbook combining explanations, mathematics, and runnable code (PyTorch, NumPy, JAX, and TensorFlow). Covers linear and softmax regression, MLPs, CNNs, RNNs, attention and transformers, and more. Adopted at 500+ universities.
Deep LearningAdvancedCreative CommonsPDF · 43 MB